
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline


import os


os.chdir('D:\Application of software')


df=pd.read_csv("D:/Application of software/ab40c054-5031-4376-b52e-9813e776f65e.csv")

df


df.head()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11 entries, 0 to 10
Data columns (total 36 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Items  Description         11 non-null     object 
 1   Duration                   11 non-null     object 
 2   Andhra Pradesh             11 non-null     float64
 3   Arunachal Pradesh          9 non-null      float64
 4   Assam                      9 non-null      float64
 5   Bihar                      9 non-null      float64
 6   Chhattisgarh               11 non-null     float64
 7   Goa                        9 non-null      float64
 8   Gujarat                    9 non-null      float64
 9   Haryana                    11 non-null     float64
 10  Himachal Pradesh           7 non-null      float64
 11  Jammu & Kashmir            9 non-null      float64
 12  Jharkhand                  9 non-null      float64
 13  Karnataka                  9 non-null      float64
 14  Kerala                     9 non-null      float64
 15  Madhya Pradesh             11 non-null     float64
 16  Maharashtra                7 non-null      float64
 17  Manipur                    7 non-null      float64
 18  Meghalaya                  11 non-null     float64
 19  Mizoram                    7 non-null      float64
 20  Nagaland                   7 non-null      float64
 21  Odisha                     11 non-null     float64
 22  Punjab                     7 non-null      float64
 23  Rajasthan                  7 non-null      float64
 24  Sikkim                     9 non-null      float64
 25  Tamil Nadu                 11 non-null     float64
 26  Telangana                  11 non-null     float64
 27  Tripura                    7 non-null      float64
 28  Uttar Pradesh              9 non-null      float64
 29  Uttarakhand                9 non-null      float64
 30  West Bengal1               0 non-null      float64
 31  Andaman & Nicobar Islands  7 non-null      float64
 32  Chandigarh                 9 non-null      float64
 33  Delhi                      11 non-null     float64
 34  Puducherry                 11 non-null     float64
 35  All_India GDP              11 non-null     float64
dtypes: float64(34), object(2)
memory usage: 3.2+ KB


df.columns

Index(['Items  Description', 'Duration', 'Andhra Pradesh ',
       'Arunachal Pradesh', 'Assam', 'Bihar', 'Chhattisgarh', 'Goa', 'Gujarat',
       'Haryana', 'Himachal Pradesh', 'Jammu & Kashmir', 'Jharkhand',
       'Karnataka', 'Kerala', 'Madhya Pradesh', 'Maharashtra', 'Manipur',
       'Meghalaya', 'Mizoram', 'Nagaland', 'Odisha', 'Punjab', 'Rajasthan',
       'Sikkim', 'Tamil Nadu', 'Telangana', 'Tripura', 'Uttar Pradesh',
       'Uttarakhand', 'West Bengal1', 'Andaman & Nicobar Islands',
       'Chandigarh', 'Delhi', 'Puducherry', 'All_India GDP'],
      dtype='object')


df1 = df[df['Duration'] != '2016-17']
df1


df1.isnull().sum()

Items  Description           0
Duration                     0
Andhra Pradesh               0
Arunachal Pradesh            0
Assam                        0
Bihar                        0
Chhattisgarh                 0
Goa                          0
Gujarat                      0
Haryana                      0
Himachal Pradesh             2
Jammu & Kashmir              0
Jharkhand                    0
Karnataka                    0
Kerala                       0
Madhya Pradesh               0
Maharashtra                  2
Manipur                      2
Meghalaya                    0
Mizoram                      2
Nagaland                     2
Odisha                       0
Punjab                       2
Rajasthan                    2
Sikkim                       0
Tamil Nadu                   0
Telangana                    0
Tripura                      2
Uttar Pradesh                0
Uttarakhand                  0
West Bengal1                 9
Andaman & Nicobar Islands    2
Chandigarh                   0
Delhi                        0
Puducherry                   0
All_India GDP                0
dtype: int64


df1.isnull().all(axis=0)

Items  Description           False
Duration                     False
Andhra Pradesh               False
Arunachal Pradesh            False
Assam                        False
Bihar                        False
Chhattisgarh                 False
Goa                          False
Gujarat                      False
Haryana                      False
Himachal Pradesh             False
Jammu & Kashmir              False
Jharkhand                    False
Karnataka                    False
Kerala                       False
Madhya Pradesh               False
Maharashtra                  False
Manipur                      False
Meghalaya                    False
Mizoram                      False
Nagaland                     False
Odisha                       False
Punjab                       False
Rajasthan                    False
Sikkim                       False
Tamil Nadu                   False
Telangana                    False
Tripura                      False
Uttar Pradesh                False
Uttarakhand                  False
West Bengal1                  True
Andaman & Nicobar Islands    False
Chandigarh                   False
Delhi                        False
Puducherry                   False
All_India GDP                False
dtype: bool


# removing West Bengal as the whole column is NAN
df1 = df1.drop('West Bengal1', axis = 1)

df1


df1.iloc[6:].isnull().sum() 
# since there are at max. only 1 missing value we can take the average of the other two numbers

Items  Description           0
Duration                     0
Andhra Pradesh               0
Arunachal Pradesh            0
Assam                        0
Bihar                        0
Chhattisgarh                 0
Goa                          0
Gujarat                      0
Haryana                      0
Himachal Pradesh             1
Jammu & Kashmir              0
Jharkhand                    0
Karnataka                    0
Kerala                       0
Madhya Pradesh               0
Maharashtra                  1
Manipur                      1
Meghalaya                    0
Mizoram                      1
Nagaland                     1
Odisha                       0
Punjab                       1
Rajasthan                    1
Sikkim                       0
Tamil Nadu                   0
Telangana                    0
Tripura                      1
Uttar Pradesh                0
Uttarakhand                  0
Andaman & Nicobar Islands    1
Chandigarh                   0
Delhi                        0
Puducherry                   0
All_India GDP                0
dtype: int64


avg_growth = df1.iloc[6:]


avg_growth #dataframe to find the average growth of states


avg_growth.columns

Index(['Items  Description', 'Duration', 'Andhra Pradesh ',
       'Arunachal Pradesh', 'Assam', 'Bihar', 'Chhattisgarh', 'Goa', 'Gujarat',
       'Haryana', 'Himachal Pradesh', 'Jammu & Kashmir', 'Jharkhand',
       'Karnataka', 'Kerala', 'Madhya Pradesh', 'Maharashtra', 'Manipur',
       'Meghalaya', 'Mizoram', 'Nagaland', 'Odisha', 'Punjab', 'Rajasthan',
       'Sikkim', 'Tamil Nadu', 'Telangana', 'Tripura', 'Uttar Pradesh',
       'Uttarakhand', 'Andaman & Nicobar Islands', 'Chandigarh', 'Delhi',
       'Puducherry', 'All_India GDP'],
      dtype='object')


avg_growth.count

<bound method DataFrame.count of               Items  Description Duration  Andhra Pradesh   Arunachal Pradesh  \
7  (% Growth over previous year)  2013-14            12.85              16.38   
8  (% Growth over previous year)  2014-15            13.40              14.79   
9  (% Growth over previous year)  2015-16            15.85              12.07   

   Assam  Bihar  Chhattisgarh    Goa  Gujarat  Haryana  ...  Tamil Nadu  \
7  13.31  12.30         16.44  -5.77    11.47    15.45  ...       13.51   
8  11.45  17.92         13.69  13.12    10.82     9.18  ...       12.51   
9  13.19  10.59         10.98  10.75    11.09    10.91  ...       10.99   

   Telangana  Tripura  Uttar Pradesh  Uttarakhand  Andaman & Nicobar Islands  \
7      12.63    18.14          14.73        13.64                      16.68   
8      13.05    15.92          10.51         8.12                      10.89   
9      12.61      NaN          10.58        13.65                        NaN   

   Chandigarh  Delhi  Puducherry  All_India GDP  
7       14.71  13.43       15.87          12.97  
8       12.33  10.96       10.14          10.65  
9        8.84  12.09       10.15           9.99  

[3 rows x 35 columns]>


# Taking only the values for the states
average_growth_values = avg_growth[avg_growth.columns[2:34]].mean()


# Sorting the average growth rate values and then making a dataframe for all the states
average_growth_values = average_growth_values.sort_values()
average_growth_rate = average_growth_values.to_frame(name='Average growth rate')
average_growth_rate


# plotting the average growth rate for all the states
plt.figure(figsize=(12,10), dpi = 300)

sns.barplot(x = average_growth_rate['Average growth rate'], y = average_growth_values.index,palette='viridis')
plt.xlabel('Average Growth Rate', fontsize=10)
plt.ylabel('States', fontsize=12)
plt.title('Average Growth Rate for all the states',fontsize=13)
plt.show()


# top 5 states as per average growth rate

average_growth_rate['Average growth rate'][-5:]

Arunachal Pradesh    14.413333
Manipur              14.610000
Nagaland             16.415000
Tripura              17.030000
Mizoram              17.700000
Name: Average growth rate, dtype: float64


# top 5 states as per average growth rate for the years 2013-14, 2014-15, 2015-16

avg_growth[['Mizoram','Tripura','Nagaland','Manipur','Arunachal Pradesh']]


#create a dataframe to store the mean and the standard deviation of the growth rate for various states

describe = pd.DataFrame(avg_growth.describe())
describe = describe.T
describe


# states having mean growth rate greater than 12 and standard deviation less than 2

describe[(describe['mean']>12) & (describe['std']<2)]


# states having mean growth rate greater than 13 and standard deviation greater than 2

describe[(describe['mean']<12) & (describe['std']>2)]


df1.head()


# filtering out the data for the year 2015-16 and storing it in a dataframe
total_GDP_15_16 = df1[(df1['Items  Description'] == 'GSDP - CURRENT PRICES (` in Crore)') & (df1['Duration'] == '2015-16')]
total_GDP_15_16


# carrying out necessary transformation to make the data ready for plotting

total_GDP_15_16_states = total_GDP_15_16[total_GDP_15_16.columns[2:34]].transpose()
total_GDP_15_16_states = total_GDP_15_16_states.rename(columns={4: 'Total GDP of States 2015-16'})
total_GDP_15_16_states = total_GDP_15_16_states.dropna()
total_GDP_15_16_states = total_GDP_15_16_states.sort_values('Total GDP of States 2015-16',ascending=True)
total_GDP_15_16_states


plt.figure(figsize=(10,8), dpi = 600)

sns.barplot(x = total_GDP_15_16_states['Total GDP of States 2015-16'], y = total_GDP_15_16_states.index,palette='plasma')
plt.xlabel('Total GDP of States for 2015-16', fontsize=15)
plt.ylabel('States', fontsize=12)
plt.title('Total GDP of States 2015-16 for all the states',fontsize=12)
plt.show()


top_5_eco = total_GDP_15_16_states[-5:]
top_5_eco


bottom_5_eco = total_GDP_15_16_states[:5]
bottom_5_eco


df1=pd.read_excel("D:\Application of software\GDP.xlsx")

df1


os.chdir('E:\states')


ap=pd.read_csv("E:/states/NAD-Andhra_Pradesh-GSVA_cur_2016-17.csv")

ap


Andhra_Pradesh=pd.read_csv("E:/states/NAD-Arunachal_Pradesh-GSVA_cur_2015-16.csv")


Arunachal_Pradesh=pd.read_csv("E:/states/NAD-Assam-GSVA_cur_2015-16.csv")


Assam=pd.read_csv("E:/states/NAD-Assam-GSVA_cur_2015-16.csv")


Bihar=pd.read_csv("E:/states/NAD-Bihar-GSVA_cur_2015-16.csv")


Chhattisgarh=pd.read_csv("E:/states/NAD-Chhattisgarh-GSVA_cur_2016-17.csv")


Goa=pd.read_csv("E:/states/NAD-Goa-GSVA_cur_2015-16.csv")


Gujarat=pd.read_csv("E:/states/NAD-Gujarat-GSVA_cur_2015-16.csv")


Haryana=pd.read_csv("E:/states/NAD-Haryana-GSVA_cur_2016-17.csv")


Himachal_Pradesh=pd.read_csv("E:/states/NAD-Himachal_Pradesh-GSVA_cur_2014-15.csv")


Jharkhand=pd.read_csv("E:/states/NAD-Jharkhand-GSVA_cur_2015-16.csv")


Karnataka=pd.read_csv("E:/states/NAD-Karnataka-GSVA_cur_2015-16.csv")


Kerala=pd.read_csv("E:/states/NAD-Kerala-GSVA_cur_2015-16.csv")


Madhya_Pradesh=pd.read_csv("E:/states/NAD-Madhya_Pradesh-GSVA_cur_2016-17.csv")


Maharashtra=pd.read_csv("E:/states/NAD-Maharashtra-GSVA_cur_2014-15.csv")


Meghalaya=pd.read_csv("E:/states/NAD-Meghalaya-GSVA_cur_2016-17.csv")


Mizoram=pd.read_csv("E:/states/NAD-Mizoram-GSVA_cur_2014-15.csv")


Nagaland=pd.read_csv("E:/states/NAD-Nagaland-GSVA_cur_2014-15.csv")


Odisha=pd.read_csv("E:/states/NAD-Odisha-GSVA_cur_2016-17.csv")


Punjab=pd.read_csv("E:/states/NAD-Punjab-GSVA_cur_2014-15.csv")


Rajasthan=pd.read_csv("E:/states/NAD-Rajasthan-GSVA_cur_2014-15.csv")


Sikkim=pd.read_csv("E:/states/NAD-Sikkim-GSVA_cur_2015-16.csv")


Tamil_Nadu=pd.read_csv("E:/states/NAD-Tamil_Nadu-GSVA_cur_2016-17.csv")


Telangana=pd.read_csv("E:/states/NAD-Telangana-GSVA_cur_2016-17.csv")


Uttar_Pradesh=pd.read_csv("E:/states/NAD-Uttar_Pradesh-GSVA_cur_2015-16.csv")


Uttarakhand=pd.read_csv("E:/states/NAD-Uttarakhand-GSVA_cur_2015-16.csv")


Manipur=pd.read_csv("E:/states/NAD-Manipur-GSVA_cur_2014-15.csv")

---------------------------------------------------------------------------
UnicodeDecodeError                        Traceback (most recent call last)
Cell In[67], line 1
----> 1 Manipur=pd.read_csv("E:/states/NAD-Manipur-GSVA_cur_2014-15.csv")

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:912, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
    899 kwds_defaults = _refine_defaults_read(
    900     dialect,
    901     delimiter,
   (...)
    908     dtype_backend=dtype_backend,
    909 )
    910 kwds.update(kwds_defaults)
--> 912 return _read(filepath_or_buffer, kwds)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:577, in _read(filepath_or_buffer, kwds)
    574 _validate_names(kwds.get("names", None))
    576 # Create the parser.
--> 577 parser = TextFileReader(filepath_or_buffer, **kwds)
    579 if chunksize or iterator:
    580     return parser

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1407, in TextFileReader.__init__(self, f, engine, **kwds)
   1404     self.options["has_index_names"] = kwds["has_index_names"]
   1406 self.handles: IOHandles | None = None
-> 1407 self._engine = self._make_engine(f, self.engine)

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\readers.py:1679, in TextFileReader._make_engine(self, f, engine)
   1676     raise ValueError(msg)
   1678 try:
-> 1679     return mapping[engine](f, **self.options)
   1680 except Exception:
   1681     if self.handles is not None:

File ~\anaconda3\Lib\site-packages\pandas\io\parsers\c_parser_wrapper.py:93, in CParserWrapper.__init__(self, src, **kwds)
     90 if kwds["dtype_backend"] == "pyarrow":
     91     # Fail here loudly instead of in cython after reading
     92     import_optional_dependency("pyarrow")
---> 93 self._reader = parsers.TextReader(src, **kwds)
     95 self.unnamed_cols = self._reader.unnamed_cols
     97 # error: Cannot determine type of 'names'

File ~\anaconda3\Lib\site-packages\pandas\_libs\parsers.pyx:550, in pandas._libs.parsers.TextReader.__cinit__()

File ~\anaconda3\Lib\site-packages\pandas\_libs\parsers.pyx:639, in pandas._libs.parsers.TextReader._get_header()

File ~\anaconda3\Lib\site-packages\pandas\_libs\parsers.pyx:850, in pandas._libs.parsers.TextReader._tokenize_rows()

File ~\anaconda3\Lib\site-packages\pandas\_libs\parsers.pyx:861, in pandas._libs.parsers.TextReader._check_tokenize_status()

File ~\anaconda3\Lib\site-packages\pandas\_libs\parsers.pyx:2021, in pandas._libs.parsers.raise_parser_error()

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa0 in position 337: invalid start byte


Tripura=pd.read_csv("E:/states/NAD-Tripura-GSVA_cur_2014-15.csv")


andhra_pradesh = Andhra_Pradesh[['S.No.','Item', '2014-15']]
andhra_pradesh = andhra_pradesh.rename(columns={'2014-15': 'Andhra_Pradesh'})

arunachal_pradesh = Arunachal_Pradesh[['S.No.','Item', '2014-15']]
arunachal_pradesh = arunachal_pradesh.rename(columns={'2014-15': 'Arunachal_Pradesh'})

assam = Assam[['S.No.','Item', '2014-15']]
assam = assam.rename(columns={'2014-15': 'Assam'})

bihar = Bihar[['S.No.','Item', '2014-15']]
bihar = bihar.rename(columns={'2014-15': 'Bihar'})

chhattisgarh = Chhattisgarh[['S.No.','Item', '2014-15']]
chhattisgarh = chhattisgarh.rename(columns={'2014-15': 'Chhattisgarh'})

goa = Goa[['S.No.','Item', '2014-15']]
goa = goa.rename(columns={'2014-15': 'Goa'})

gujarat = Gujarat[['S.No.','Item', '2014-15']]
gujarat = gujarat.rename(columns={'2014-15': 'Gujarat'})

haryana = Haryana[['S.No.','Item', '2014-15']]
haryana = haryana.rename(columns={'2014-15': 'Haryana'})

himachal_Pradesh = Himachal_Pradesh[['S.No.','Item', '2014-15']]
himachal_Pradesh = himachal_Pradesh.rename(columns={'2014-15': 'Himachal_Pradesh'})

jharkhand = Jharkhand[['S.No.','Item', '2014-15']]
jharkhand = jharkhand.rename(columns={'2014-15': 'Jharkhand'})

karnataka = Karnataka[['S.No.','Item', '2014-15']]
karnataka = karnataka.rename(columns={'2014-15': 'Karnataka'})

kerala = Kerala[['S.No.','Item', '2014-15']]
kerala = kerala.rename(columns={'2014-15': 'Kerala'})

madhya_pradesh = Madhya_Pradesh[['S.No.','Item', '2014-15']]
madhya_pradesh = madhya_pradesh.rename(columns={'2014-15': 'Madhya_Pradesh'})

maharashtra = Maharashtra[['S.No.','Item', '2014-15']]
maharashtra = maharashtra.rename(columns={'2014-15': 'Maharashtra'})



meghalaya = Meghalaya[['S.No.','Item', '2014-15']]
meghalaya = meghalaya.rename(columns={'2014-15': 'Meghalaya'})

mizoram = Mizoram[['S.No.','Item', '2014-15']]
mizoram = mizoram.rename(columns={'2014-15': 'Mizoram'})

nagaland = Nagaland[['S.No.','Item', '2014-15']]
nagaland = nagaland.rename(columns={'2014-15': 'Nagaland'})

odisha = Odisha[['S.No.','Item', '2014-15']]
odisha = odisha.rename(columns={'2014-15': 'Odisha'})

punjab = Punjab[['S.No.','Item', '2014-15']]
punjab = punjab.rename(columns={'2014-15': 'Punjab'})

rajasthan = Rajasthan[['S.No.','Item', '2014-15']]
rajasthan = rajasthan.rename(columns={'2014-15': 'Rajasthan'})

sikkim = Sikkim[['S.No.','Item', '2014-15']]
sikkim = sikkim.rename(columns={'2014-15': 'Sikkim'})

tamil_nadu = Tamil_Nadu[['S.No.','Item', '2014-15']]
tamil_nadu = tamil_nadu.rename(columns={'2014-15': 'Tamil_Nadu'})

telangana = Telangana[['S.No.','Item', '2014-15']]
telangana = telangana.rename(columns={'2014-15': 'Telangana'})

tripura = Tripura[['S.No.','Item', '2014-15']]
tripura = tripura.rename(columns={'2014-15': 'Tripura'})

uttar_pradesh = Uttar_Pradesh[['S.No.','Item', '2014-15']]
uttar_pradesh = uttar_pradesh.rename(columns={'2014-15': 'Uttar_Pradesh'})

uttarakhand = Uttarakhand[['S.No.','Item', '2014-15']]
uttarakhand = uttarakhand.rename(columns={'2014-15': 'Uttarakhand'})


dfs = [andhra_pradesh,arunachal_pradesh, assam, bihar, chhattisgarh, goa, gujarat, haryana,himachal_Pradesh,
       jharkhand, karnataka,kerala,madhya_pradesh, maharashtra,meghalaya,mizoram, nagaland,odisha,
       punjab,rajasthan,sikkim,tamil_nadu,telangana,tripura,uttarakhand, uttar_pradesh]


from functools import reduce
df_final = reduce(lambda left,right: pd.merge(left,right,how ='left',on=['S.No.', 'Item']), dfs)


df_final.columns

Index(['S.No.', 'Item', 'Andhra_Pradesh', 'Arunachal_Pradesh', 'Assam',
       'Bihar', 'Chhattisgarh', 'Goa', 'Gujarat', 'Haryana',
       'Himachal_Pradesh', 'Jharkhand', 'Karnataka', 'Kerala',
       'Madhya_Pradesh', 'Maharashtra', 'Meghalaya', 'Mizoram', 'Nagaland',
       'Odisha', 'Punjab', 'Rajasthan', 'Sikkim', 'Tamil_Nadu', 'Telangana',
       'Tripura', 'Uttarakhand', 'Uttar_Pradesh'],
      dtype='object')


# Renaming some of the state names for merging data at a later stage

df_final = df_final.rename(columns={'Andhra_Pradesh':'Andhra Pradesh', 'Arunachal_Pradesh':'Arunachal Pradesh',
                                   'Himachal_Pradesh':'Himachal Pradesh','Madhya_Pradesh':'Madhya Pradesh',
                                   'Tamil_Nadu':'Tamil Nadu','Uttar_Pradesh':'Uttar Pradesh',
                                   'Chhattisgarh':'Chhatisgarh','Uttarakhand':'Uttrakhand'})


# Final dataframe having the data for all the states for all the sectors and subsectors of the economy

df_final


gdp_per_capita = df_final.iloc[32][2:].sort_values()
gdp_per_capita = gdp_per_capita.to_frame(name = 'GDP per capita')
gdp_per_capita


plt.figure(figsize=(12,8), dpi=600)                             

sns.barplot(x = gdp_per_capita['GDP per capita'], y =gdp_per_capita.index, palette='Reds' )
plt.xlabel('GDP per capita', fontsize=12)
plt.ylabel('States', fontsize=12)
plt.title('GDP per capita vs States',fontsize=12)
plt.show()


top_5_gdp_per_capita = gdp_per_capita[-5:]
top_5_gdp_per_capita


bottom_5_gdp_per_capita = gdp_per_capita[:5]
bottom_5_gdp_per_capita


ratio = gdp_per_capita['GDP per capita'].max()/gdp_per_capita['GDP per capita'].min()
print('The Ratio of highest per capita GDP to the lowest per capita GDP is: ',ratio)

The Ratio of highest per capita GDP to the lowest per capita GDP is:  8.004741709371503


# Identifying the Primary, Secondary and the tertiary sectors and concating these to form a dataframe

primary = df_final[df_final['Item']=='Primary']
secondary = df_final[df_final['Item']=='Secondary']
tertiary = df_final[df_final['Item']=='Tertiary']
gdp = df_final[df_final['Item']=='Gross State Domestic Product']

pst = pd.concat([primary, secondary,tertiary,gdp], axis = 0).reset_index()
pst =  pst.drop(['index','S.No.'], axis = 1).set_index('Item')

pst


# calculating the percentage contribution of each sector to the Gross State Domestic Product for each state

pst.loc['primary_percentage'] = pst.loc['Primary'] / pst.loc['Gross State Domestic Product'] * 100
pst.loc['secondary_percentage'] = pst.loc['Secondary'] / pst.loc['Gross State Domestic Product'] * 100
pst.loc['tertiary_percentage'] = pst.loc['Tertiary'] / pst.loc['Gross State Domestic Product'] * 100

pst


# Transposing the dataframe for better readability

pst = pst.T
pst = pst.sort_values('Gross State Domestic Product')
pst


plt.figure(figsize=(12,10), dpi =600)

bars1 = pst['primary_percentage']
bars2 = pst['secondary_percentage']
bars3 = pst['tertiary_percentage']
 
legends = ['Primary %', 'Secondary %', 'Tertiary %']

bars = np.add(bars1, bars2).tolist()
 
r = np.arange(0,len(pst.index))
 
names = pst.index
barWidth = 1
 
# Create red bars
plt.bar(r, bars1, color='red', edgecolor='white')
# Create green bars (middle), on top of the firs ones
plt.bar(r, bars2, bottom=bars1, color='green', edgecolor='white')
# Create blue bars (top)
plt.bar(r, bars3, bottom=bars, color='blue', edgecolor='white')
 
plt.xticks(r, names,rotation=90)
plt.xlabel('States',fontsize=12)
plt.ylabel('Percentage contribution to GDP',fontsize=12)
plt.title('Percentage contribution of the Primary, Secondary and Tertiary sectors as a percentage of the total GDP for all the states')

plt.legend(legends)

plt.tight_layout()


gdp_per_capita


# States between the 85th and 100th percentile

C1 = gdp_per_capita[gdp_per_capita['GDP per capita'] > gdp_per_capita['GDP per capita'].quantile(0.85)]
C1


# States between the 50th and 85th percentile

C2 = gdp_per_capita[(gdp_per_capita['GDP per capita'] > gdp_per_capita['GDP per capita'].quantile(0.50)) & (gdp_per_capita['GDP per capita'] < gdp_per_capita['GDP per capita'].quantile(0.85))]
C2


# States between the 20th and 50th percentile

C3 = gdp_per_capita[(gdp_per_capita['GDP per capita'] > gdp_per_capita['GDP per capita'].quantile(0.20)) & (gdp_per_capita['GDP per capita'] <= gdp_per_capita['GDP per capita'].quantile(0.50))]
C3


# States below the 20th percentile

C4 = gdp_per_capita[gdp_per_capita['GDP per capita'] < gdp_per_capita['GDP per capita'].quantile(0.20)]
C4


C1_df = df_final[['S.No.','Item']+list(states for states in C1.index)]
C2_df = df_final[['S.No.','Item']+list(states for states in C2.index)]
C3_df = df_final[['S.No.','Item']+list(states for states in C3.index)]
C4_df = df_final[['S.No.','Item']+list(states for states in C4.index)]


C1_df = C1_df.iloc[[0,5,7,8,9,11,14,22,23,24,25,30,32]]
C2_df = C2_df.iloc[[0,5,7,8,9,11,14,22,23,24,25,30,32]]
C3_df = C3_df.iloc[[0,5,7,8,9,11,14,22,23,24,25,30,32]]
C4_df = C4_df.iloc[[0,5,7,8,9,11,14,22,23,24,25,30,32]]


C1_df


# Creating the column for Total values for all sub-sectors for all the states and the column for the percentage contribution
# to the total GSDP by each of the sub-sectors for all the states

C1_df['Total for all states'] = C1_df['Kerala']+C1_df['Haryana']+C1_df['Sikkim']+C1_df['Goa']
C1_df['Percentage of Total GDP'] = C1_df['Total for all states']/C1_df['Total for all states'][11] * 100
C1_df


# Identifying the major sub-sectors contributing more to the GSDP  by finding the cumulative sum

C1_contributor = C1_df[['Item','Percentage of Total GDP']][:-2].sort_values(by='Percentage of Total GDP', ascending=False)
C1_contributor.reset_index(drop=True, inplace=True)
C1_contributor['Cumulative sum'] = C1_contributor['Percentage of Total GDP'].cumsum()
C1_contributor


plt.figure(figsize=(6,4), dpi=600)
sns.barplot(y=C1_contributor['Item'], x = C1_contributor['Percentage of Total GDP'], palette='inferno')
plt.xlabel("Percentage of Total GSDP for C1 States")
plt.ylabel('Sub-sectors')
plt.title('Percentage of Total GSDP for C1 States vs Sub-sectors')
plt.savefig("Percentage of Total GSDP for C1 States vs Sub-sectors.png", bbox_inches='tight', dpi=600)

plt.show()


C2_df['Total for all states']=list(C2_df[list(states for states in C2_df.columns)[2:]].sum(axis=1))
C2_df['Percentage of Total GDP'] = C2_df['Total for all states']/C2_df['Total for all states'][11] * 100
C2_contributor = C2_df[['Item','Percentage of Total GDP']][:-2].sort_values(by='Percentage of Total GDP', ascending=False)
C2_contributor.reset_index(drop=True, inplace=True)
C2_contributor['Cumulative sum'] = C2_contributor['Percentage of Total GDP'].cumsum()
C2_contributor


plt.figure(figsize=(6,4), dpi=600)
sns.barplot(y=C2_contributor['Item'], x = C2_contributor['Percentage of Total GDP'],palette='hot')
plt.xlabel("Percentage of Total GSDP for C2 States")
plt.ylabel('Sub-sectors')
plt.title('Percentage of Total GSDP for C2 States vs Sub-sectors')
plt.show()


C3_df['Total for all states']=list(C3_df[list(states for states in C3_df.columns)[2:]].sum(axis=1))
C3_df['Percentage of Total GDP'] = C3_df['Total for all states']/C3_df['Total for all states'][11] * 100
C3_contributor = C3_df[['Item','Percentage of Total GDP']][:-2].sort_values(by='Percentage of Total GDP', ascending=False)
C3_contributor.reset_index(drop=True, inplace=True)
C3_contributor['Cumulative sum'] = C3_contributor['Percentage of Total GDP'].cumsum()
C3_contributor


plt.figure(figsize=(6,4), dpi=600)
sns.barplot(y=C3_contributor['Item'], x = C3_contributor['Percentage of Total GDP'], palette='autumn')
plt.xlabel("Percentage of Total GSDP for C3 States")
plt.ylabel('Sub-sectors')
plt.title('Percentage of Total GSDP for C3 States vs Sub-sectors')

plt.show()


C4_df['Total for all states']=list(C4_df[list(states for states in C4_df.columns)[2:]].sum(axis=1))
C4_df['Percentage of Total GDP'] = C4_df['Total for all states']/C4_df['Total for all states'][11] * 100
C4_contributor = C4_df[['Item','Percentage of Total GDP']][:-2].sort_values(by='Percentage of Total GDP', ascending=False)
C4_contributor.reset_index(drop=True, inplace=True)
C4_contributor['Cumulative sum'] = C4_contributor['Percentage of Total GDP'].cumsum()
C4_contributor


plt.figure(figsize=(6,4), dpi=600)
sns.barplot(y=C4_contributor['Item'], x = C4_contributor['Percentage of Total GDP'], palette='spring')
plt.xlabel("Percentage of Total GSDP for C4 States")
plt.ylabel('Sub-sectors')
plt.title('Percentage of Total GSDP for C4 States vs Sub-sectors')

plt.show()


# Reading the data and selecting the data for the year 2014-14 and the education level for Primary, Upper Primary and Secondary

data2 = pd.read_csv("D:/Application of software/rs_session243_au570_1.1.csv")
data2 = data2[['Level of Education - State','Primary - 2014-2015.1','Upper Primary - 2014-2015','Secondary - 2014-2015']]
data2


# Dropping rows of data which we don not need like Union Territories and for which we don't have GDP per-capita available like West Bengal

data2 =  data2.drop([0,5,7,8,9,14,18,26,35,36])
data2 = data2.reset_index(drop = True)
data2=data2.rename(columns={'Level of Education - State': 'State'})


# Necessary transformation like resetting the index and renaming the column name for merging with another dataframe

states_gdp_per_capita = gdp_per_capita.reset_index()
states_gdp_per_capita=states_gdp_per_capita.rename(columns={'index':'State'})


data2_final = pd.merge(data2,states_gdp_per_capita,how='left',on=['State'])


data2_final = data2_final.rename(columns={'State':'Level of education - State'})


# Final dataframe having the education level dropout rates for all the states and the GDP per capita

data2_final


data2_final.describe()


import seaborn as sns
import matplotlib.pyplot as plt


# Primary - 2014-2015.1

plt.figure(figsize=(8,6), dpi= 600)

sns.scatterplot(y=data2_final['GDP per capita'],x=data2_final['Primary - 2014-2015.1'])
plt.xlabel('Primary Drop out rate')
plt.ylabel('Per capita GDP')
plt.title('Per capita GDP vs Primary Drop out rate')
plt.show()


# Upper Primary - 2014-2015

plt.figure(figsize=(8,6), dpi= 600)

sns.scatterplot(y=data2_final['GDP per capita'],x=data2_final['Upper Primary - 2014-2015'])
plt.xlabel('Upper Primary Drop out rate')
plt.ylabel('Per capita GDP')
plt.title('Per capita GDP vs Upper Primary Drop out rate')
plt.show()


# Secondary - 2014-2015

plt.figure(figsize=(8,6), dpi= 100)

sns.scatterplot(y=data2_final['GDP per capita'],x=data2_final['Secondary - 2014-2015'])
plt.xlabel('Secondary Drop out rate')
plt.ylabel('Per capita GDP')
plt.title('Per capita GDP vs Secondary Drop out rate')
plt.show()

	Items Description	Duration	Andhra Pradesh	Arunachal Pradesh	Assam	Bihar	Chhattisgarh	Goa	Gujarat	Haryana	...	Telangana	Tripura	Uttar Pradesh	Uttarakhand	West Bengal1	Andaman & Nicobar Islands	Chandigarh	Delhi	Puducherry	All_India GDP
0	GSDP - CURRENT PRICES (` in Crore)	2011-12	379402.0	11063.0	143175.0	247144.0	158074.0	42367.0	615606.0	297539.0	...	359433.0	19208.0	724049.0	115523.0	NaN	3979.0	18768.0	343767.0	16818.0	8736039.0
1	GSDP - CURRENT PRICES (` in Crore)	2012-13	411404.0	12547.0	156864.0	282368.0	177511.0	38120.0	724495.0	347032.0	...	401493.0	21663.0	822903.0	131835.0	NaN	4421.0	21609.0	391238.0	18875.0	9946636.0
2	GSDP - CURRENT PRICES (` in Crore)	2013-14	464272.0	14602.0	177745.0	317101.0	206690.0	35921.0	807623.0	400662.0	...	452186.0	25593.0	944146.0	149817.0	NaN	5159.0	24787.0	443783.0	21870.0	11236635.0
3	GSDP - CURRENT PRICES (` in Crore)	2014-15	526468.0	16761.0	198098.0	373920.0	234982.0	40633.0	895027.0	437462.0	...	511178.0	29667.0	1043371.0	161985.0	NaN	5721.0	27844.0	492424.0	24089.0	12433749.0
4	GSDP - CURRENT PRICES (` in Crore)	2015-16	609934.0	18784.0	224234.0	413503.0	260776.0	45002.0	994316.0	485184.0	...	575631.0	NaN	1153795.0	184091.0	NaN	NaN	30304.0	551963.0	26533.0	13675331.0

	Items Description	Duration	Andhra Pradesh	Arunachal Pradesh	Assam	Bihar	Chhattisgarh	Goa	Gujarat	Haryana	...	Tamil Nadu	Telangana	Tripura	Uttar Pradesh	Uttarakhand	Andaman & Nicobar Islands	Chandigarh	Delhi	Puducherry	All_India GDP
0	GSDP - CURRENT PRICES (` in Crore)	2011-12	379402.00	11063.00	143175.00	247144.00	158074.00	42367.00	615606.00	297539.00	...	751485.00	359433.00	19208.00	724049.00	115523.00	3979.00	18768.00	343767.00	16818.00	8736039.00
1	GSDP - CURRENT PRICES (` in Crore)	2012-13	411404.00	12547.00	156864.00	282368.00	177511.00	38120.00	724495.00	347032.00	...	855481.00	401493.00	21663.00	822903.00	131835.00	4421.00	21609.00	391238.00	18875.00	9946636.00
2	GSDP - CURRENT PRICES (` in Crore)	2013-14	464272.00	14602.00	177745.00	317101.00	206690.00	35921.00	807623.00	400662.00	...	971090.00	452186.00	25593.00	944146.00	149817.00	5159.00	24787.00	443783.00	21870.00	11236635.00
3	GSDP - CURRENT PRICES (` in Crore)	2014-15	526468.00	16761.00	198098.00	373920.00	234982.00	40633.00	895027.00	437462.00	...	1092564.00	511178.00	29667.00	1043371.00	161985.00	5721.00	27844.00	492424.00	24089.00	12433749.00
4	GSDP - CURRENT PRICES (` in Crore)	2015-16	609934.00	18784.00	224234.00	413503.00	260776.00	45002.00	994316.00	485184.00	...	1212668.00	575631.00	NaN	1153795.00	184091.00	NaN	30304.00	551963.00	26533.00	13675331.00
6	(% Growth over previous year)	2012-13	8.43	13.41	9.56	14.25	12.30	-10.02	17.69	16.63	...	13.84	11.70	12.78	13.65	14.12	11.13	15.14	13.81	12.23	13.86
7	(% Growth over previous year)	2013-14	12.85	16.38	13.31	12.30	16.44	-5.77	11.47	15.45	...	13.51	12.63	18.14	14.73	13.64	16.68	14.71	13.43	15.87	12.97
8	(% Growth over previous year)	2014-15	13.40	14.79	11.45	17.92	13.69	13.12	10.82	9.18	...	12.51	13.05	15.92	10.51	8.12	10.89	12.33	10.96	10.14	10.65
9	(% Growth over previous year)	2015-16	15.85	12.07	13.19	10.59	10.98	10.75	11.09	10.91	...	10.99	12.61	NaN	10.58	13.65	NaN	8.84	12.09	10.15	9.99

	count	mean	std	min	25%	50%	75%	max
Andhra Pradesh	3.0	14.033333	1.597133	12.85	13.1250	13.400	14.6250	15.85
Arunachal Pradesh	3.0	14.413333	2.179549	12.07	13.4300	14.790	15.5850	16.38
Assam	3.0	12.650000	1.040961	11.45	12.3200	13.190	13.2500	13.31
Bihar	3.0	13.603333	3.834871	10.59	11.4450	12.300	15.1100	17.92
Chhattisgarh	3.0	13.703333	2.730024	10.98	12.3350	13.690	15.0650	16.44
Goa	3.0	6.033333	10.290444	-5.77	2.4900	10.750	11.9350	13.12
Gujarat	3.0	11.126667	0.326548	10.82	10.9550	11.090	11.2800	11.47
Haryana	3.0	11.846667	3.238245	9.18	10.0450	10.910	13.1800	15.45
Himachal Pradesh	2.0	12.280000	3.026417	10.14	11.2100	12.280	13.3500	14.42
Jammu & Kashmir	3.0	10.900000	6.642146	4.70	7.3950	10.090	14.0000	17.91
Jharkhand	3.0	11.500000	3.610374	7.92	9.6800	11.440	13.2900	15.14
Karnataka	3.0	14.120000	3.624969	11.42	12.0600	12.700	15.4700	18.24
Kerala	3.0	12.583333	0.654930	11.85	12.3200	12.790	12.9500	13.11
Madhya Pradesh	3.0	12.626667	2.408492	10.11	11.4850	12.860	13.8850	14.91
Maharashtra	2.0	11.260000	3.507250	8.78	10.0200	11.260	12.5000	13.74
Manipur	2.0	14.610000	4.553768	11.39	13.0000	14.610	16.2200	17.83
Meghalaya	3.0	6.953333	2.401548	4.87	5.6400	6.410	7.9950	9.58
Mizoram	2.0	17.700000	7.636753	12.30	15.0000	17.700	20.4000	23.10
Nagaland	2.0	16.415000	7.870098	10.85	13.6325	16.415	19.1975	21.98
Odisha	3.0	9.836667	3.411412	6.19	8.2800	10.370	11.6600	12.95
Punjab	2.0	11.185000	1.746554	9.95	10.5675	11.185	11.8025	12.42
Rajasthan	2.0	11.320000	0.070711	11.27	11.2950	11.320	11.3450	11.37
Sikkim	3.0	10.486667	1.622108	9.39	9.5550	9.720	11.0350	12.35
Tamil Nadu	3.0	12.336667	1.268910	10.99	11.7500	12.510	13.0100	13.51
Telangana	3.0	12.763333	0.248462	12.61	12.6200	12.630	12.8400	13.05
Tripura	2.0	17.030000	1.569777	15.92	16.4750	17.030	17.5850	18.14
Uttar Pradesh	3.0	11.940000	2.416464	10.51	10.5450	10.580	12.6550	14.73
Uttarakhand	3.0	11.803333	3.189864	8.12	10.8800	13.640	13.6450	13.65
Andaman & Nicobar Islands	2.0	13.785000	4.094148	10.89	12.3375	13.785	15.2325	16.68
Chandigarh	3.0	11.960000	2.952440	8.84	10.5850	12.330	13.5200	14.71
Delhi	3.0	12.160000	1.236487	10.96	11.5250	12.090	12.7600	13.43
Puducherry	3.0	12.053333	3.305334	10.14	10.1450	10.150	13.0100	15.87
All_India GDP	3.0	11.203333	1.565162	9.99	10.3200	10.650	11.8100	12.97

	count	mean	std	min	25%	50%	75%	max
Goa	3.0	6.033333	10.290444	-5.77	2.490	10.75	11.935	13.12
Haryana	3.0	11.846667	3.238245	9.18	10.045	10.91	13.180	15.45
Jammu & Kashmir	3.0	10.900000	6.642146	4.70	7.395	10.09	14.000	17.91
Jharkhand	3.0	11.500000	3.610374	7.92	9.680	11.44	13.290	15.14
Maharashtra	2.0	11.260000	3.507250	8.78	10.020	11.26	12.500	13.74
Meghalaya	3.0	6.953333	2.401548	4.87	5.640	6.41	7.995	9.58
Odisha	3.0	9.836667	3.411412	6.19	8.280	10.37	11.660	12.95
Uttar Pradesh	3.0	11.940000	2.416464	10.51	10.545	10.58	12.655	14.73
Uttarakhand	3.0	11.803333	3.189864	8.12	10.880	13.64	13.645	13.65
Chandigarh	3.0	11.960000	2.952440	8.84	10.585	12.33	13.520	14.71

	Items Description	Duration	Andhra Pradesh	Arunachal Pradesh	Assam	Bihar	Chhattisgarh	Goa	Gujarat	Haryana	...	Tamil Nadu	Telangana	Tripura	Uttar Pradesh	Uttarakhand	Andaman & Nicobar Islands	Chandigarh	Delhi	Puducherry	All_India GDP
0	GSDP - CURRENT PRICES (` in Crore)	2011-12	379402.0	11063.0	143175.0	247144.0	158074.0	42367.0	615606.0	297539.0	...	751485.0	359433.0	19208.0	724049.0	115523.0	3979.0	18768.0	343767.0	16818.0	8736039.0
1	GSDP - CURRENT PRICES (` in Crore)	2012-13	411404.0	12547.0	156864.0	282368.0	177511.0	38120.0	724495.0	347032.0	...	855481.0	401493.0	21663.0	822903.0	131835.0	4421.0	21609.0	391238.0	18875.0	9946636.0
2	GSDP - CURRENT PRICES (` in Crore)	2013-14	464272.0	14602.0	177745.0	317101.0	206690.0	35921.0	807623.0	400662.0	...	971090.0	452186.0	25593.0	944146.0	149817.0	5159.0	24787.0	443783.0	21870.0	11236635.0
3	GSDP - CURRENT PRICES (` in Crore)	2014-15	526468.0	16761.0	198098.0	373920.0	234982.0	40633.0	895027.0	437462.0	...	1092564.0	511178.0	29667.0	1043371.0	161985.0	5721.0	27844.0	492424.0	24089.0	12433749.0
4	GSDP - CURRENT PRICES (` in Crore)	2015-16	609934.0	18784.0	224234.0	413503.0	260776.0	45002.0	994316.0	485184.0	...	1212668.0	575631.0	NaN	1153795.0	184091.0	NaN	30304.0	551963.0	26533.0	13675331.0

	Mizoram	Tripura	Nagaland	Manipur	Arunachal Pradesh
7	23.1	18.14	21.98	17.83	16.38
8	12.3	15.92	10.85	11.39	14.79
9	NaN	NaN	NaN	NaN	12.07

	Total GDP of States 2015-16
Sikkim	16637.0
Arunachal Pradesh	18784.0
Puducherry	26533.0
Meghalaya	26745.0
Chandigarh	30304.0
Goa	45002.0
Jammu & Kashmir	118387.0
Uttarakhand	184091.0
Assam	224234.0
Jharkhand	241955.0
Chhattisgarh	260776.0
Odisha	341887.0
Bihar	413503.0
Haryana	485184.0
Madhya Pradesh	543975.0
Delhi	551963.0
Telangana	575631.0
Kerala	588337.0
Andhra Pradesh	609934.0
Gujarat	994316.0
Karnataka	1027068.0
Uttar Pradesh	1153795.0
Tamil Nadu	1212668.0

	States	Year_2020_21
0	Andhra Pradesh	105880
1	Arunachal Pradesh	106903
2	Assam	61304
3	Bihar	28127
4	Chhattisgarh	73259
5	Goa	298527
6	Gujarat	160321
7	Haryana	155756
8	Himachal Pradesh	133079
9	Jharkhand	51365
10	Karnataka	149030
11	Kerala	132700
12	Madhya Pradesh	56320
13	Maharashtra	127970
14	Manipur	46968
15	Meghalaya	56679
16	Mizoram	118555
17	Nagaland	72215
18	Odisha	70877
19	Punjab	111662
20	Rajasthan	73140
21	Sikkim	239928
22	Tamil Nadu	143528
23	Telangana	139640
24	Tripura	80512
25	Uttar Pradesh	39298
26	Uttarakhand	137987
27	West Bengal	63562
28	Andaman & Nicobar Islands	152859
29	Chandigarh	199845
30	Delhi	234569
31	Jammu & Kashmir	65172
32	Puducherry	144866

	S.No.	Item	2011-12	2012-13	2013-14	2014-15	2015-16	2016-17
0	1	Agriculture, forestry and fishing	9400805	11186428	12895568	14819416	17326726	20386004
1	1.1	Crops	5204052	6123041	7114707	7893514	8644285	9717089
2	1.2	Livestock	2758776	3358438	3643026	4309078	5155487	5979648
3	1.3	Forestry and logging	250314	253029	280493	346160	340550	335487
4	1.4	Fishing and aquaculture	1187663	1451920	1857342	2270664	3186404	4353780
5	2	Mining and quarrying	1416194	1459027	1419200	1484300	1633100	1757565
6	Total	Primary	10816999	12645455	14314768	16303716	18959826	22143569
7	3	Manufacturing	5070622	4237657	4242337	4672266	5078685	5740816
8	4	Electricity, gas, water supply & other utility...	1076517	713029	1014130	1151729	1251910	1298947
9	5	Construction	3702645	3759004	4065131	4664889	4986189	5467732
10	Total	Secondary	9849784	8709690	9321598	10488884	11316784	12507496
11	6	Trade, repair, hotels and restaurants	2673600	3179200	3784900	4233400	4577700	5174600
12	6.1	Trade & repair services	2322700	2775600	3290800	3716000	3964000	4382200
13	6.2	Hotels & restaurants	350900	403600	494100	517400	613700	792400
14	7	Transport, storage, communication & services r...	3251379	3778632	4365811	5076984	5806226	6766317
15	7.1	Railways	264279	303702	334468	424228	436596	445372
16	7.2	Road transport	1824800	2200400	2516200	2816000	3217400	3798300
17	7.3	Water transport	95100	90400	60600	94200	110500	123600
18	7.4	Air transport	4500	10600	5100	14900	25000	28000
19	7.5	Services incidental to transport	501800	535500	634700	780200	904200	1020800
20	7.6	Storage	20700	16600	18700	18700	19000	20600
21	7.7	Communication & services related to broadcasting	540200	621430	796043	928756	1093530	1329645
22	8	Financial services	1425608	1584297	1710463	1900863	2125508	2415390
23	9	Real estate, ownership of dwelling & professio...	2833805	3434098	3897345	4405409	5092310	5942431
24	10	Public administration	1393752	1552379	1738971	2200897	2594904	3039676
25	11	Other services	2730376	3063920	3538298	4215389	5248604	6180240
26	Total	Tertiary	14308520	16592526	19035788	22032942	25445252	29518654
27	12	TOTAL GSVA at basic prices	34975303	37947671	42672154	48825542	55721862	64169719
28	13	Taxes on Products	4243900	4656500	5263500	5512100	6719300	7236500
29	14	Subsidies on products	1279000	1463800	1508500	1690800	1447800	1475500
30	15	Gross State Domestic Product	37940203	41140371	46427154	52646842	60993362	69930719
31	16	Population ('00)	492750	495660	498570	501510	504460	507430
32	17	Per Capita GSDP (Rs.)	76997	83001	93121	104977	120908	137814

	S.No.	Item	Andhra Pradesh	Arunachal Pradesh	Assam	Bihar	Chhatisgarh	Goa	Gujarat	Haryana	...	Nagaland	Odisha	Punjab	Rajasthan	Sikkim	Tamil Nadu	Telangana	Tripura	Uttrakhand	Uttar Pradesh
0	1	Agriculture, forestry and fishing	686117	3855548	3855548	7951890	3948847	308507	13769969.00	8015238.0	...	607897	6422978	9285716	15044394	137447	13064238.0	7591501	799825.0	1601423.0	25097754
1	1.1	Crops	415520	2890544	2890544	4688237	2613371	140421	9671086.00	4636731.0	...	375825	4382636	5690972	7673441	114976	7297820.0	4162493	397591.0	866146.0	16215212
2	1.2	Livestock	38387	173478	173478	2060296	352208	30141	2698910.00	2916173.0	...	123800	788243	2638842	5356257	17338	4693361.0	2951299	88176.0	391188.0	7096876
3	1.3	Forestry and logging	224017	261987	261987	550132	597785	15744	761616.00	352254.0	...	99802	791463	848245	1956660	4529	392705.0	210741	145096.0	339293.0	1404936
4	1.4	Fishing and aquaculture	8193	529539	529539	653224	385483	122201	638357.00	110080.0	...	8470	460636	107657	58036	604	680352.0	266968	168961.0	4796.0	380730
5	2	Mining and quarrying	30842	1471149	1471149	68107	2451970	3622	2117218.00	25186.0	...	8280	2586328	10354	4069385	1329	265536.0	1541853	142391.0	244549.0	901501
6	Total	Primary	716959	5326697	5326697	8019997	6400817	312129	15887187.00	8040424.0	...	616178	9009306	9296070	19113780	138776	13329774.0	9133354	942216.0	1845972.0	25999255
7	3	Manufacturing	26120	2002936	2002936	2189965	4370593	1177608	24087538.00	7756921.0	...	18346	5754229	4790341	6552580	550697	18914794.0	6353711	228625.0	5866252.0	12261649
8	4	Electricity, gas, water supply & other utility...	113527	296587	296587	345168	1198438	204110	3409983.00	1101919.0	...	37944	833067	911611	1122888	212499	1710379.0	716266	77870.0	433880.0	2030625
9	5	Construction	147842	1733568	1733568	3449763	2669855	165819	5526017.00	3702571.0	...	156072	2402396	2202962	5353326	82058	12216718.0	2854024	177899.0	1342733.0	11256450
10	Total	Secondary	287489	4033091	4033091	5984896	8238886	1547536	33023538.00	12561411.0	...	212361	8989693	7904914	13028794	845253	32841892.0	9924001	484393.0	7642865.0	25548724
11	6	Trade, repair, hotels and restaurants	60421	2987155	2987155	7448373	1535571	380927	10178713.00	4986319.0	...	140781	3149555	4419919	7297290	70568	12895842.0	6494607	390423.0	1743106.0	9437243
12	6.1	Trade & repair services	56796	2876251	2876251	7081391	1414164	343492	10178713.00	4817784.0	...	134174	2886789	4201252	6942748	64624	11252588.0	5724128	390423.0	1534073.0	8476139
13	6.2	Hotels & restaurants	3625	110904	110904	366982	121407	37434	NaN	168535.0	...	6607	262766	218667	354543	5945	1643253.0	770479	NaN	209033.0	961104
14	7	Transport, storage, communication & services r...	35203	1194568	1194568	3147173	871770	189656	4555910.00	2560623.0	...	77521	2034016	1951809	3814461	47347	7188320.0	3604741	155956.0	1066693.0	7404509
15	7.1	Railways	59	252509	252509	462413	159176	15649	511593.00	423873.0	...	336	341494	233389	464638	0	468553.0	199686	305.0	21295.0	1618742
16	7.2	Road transport	15467	507668	507668	1572288	386628	46171	NaN	1452364.0	...	34548	973144	928575	2121206	35283	3660994.0	2055658	NaN	NaN	3645747
17	7.3	Water transport	0	4502	4502	2228	0	17820	NaN	NaN	...	600	50349	0	0	0	70414.0	0	NaN	NaN	681
18	7.4	Air transport	0	26223	26223	13599	9507	46359	NaN	NaN	...	4153	15354	4473	13469	0	180836.0	120691	NaN	3889.0	36582
19	7.5	Services incidental to transport	109	35739	35739	166600	5232	19272	NaN	190269.0	...	0	117469	48124	47609	0	NaN	454909	NaN	-76.0	16323
20	7.6	Storage	0	10308	10308	10618	16675	357	57634.00	14459.0	...	89	22675	76429	16584	0	39834.0	19805	254.0	660.0	171696
21	7.7	Communication & services related to broadcasting	19568	357619	357619	919427	294552	44028	1242520.00	479658.0	...	37794	513531	660819	1150955	12064	1903283.0	753992	66676.0	733778.0	1914737
22	8	Financial services	25207	543651	543651	1178022	739057	233618	4606644.00	1671486.0	...	60393	1065147	2057520	1827413	21079	5598498.0	3023729	86094.0	385030.0	3392275
23	9	Real estate, ownership of dwelling & professio...	48418	1412466	1412466	3740641	2462166	407099	5179502.00	6970183.0	...	159651	2348714	3142786	6451997	75330	16830213.0	9478839	190704.0	831307.0	14548185
24	10	Public administration	243867	1373611	1373611	2078171	867982	346486	2576195.00	1036377.0	...	295424	1318221	1842730	2460364	119514	3400800.0	1711265	338244.0	579409.0	6152124
25	11	Other services	218728	1795658	1795658	4587589	1112232	180431	3123413.00	2001581.0	...	259186	2340603	3303041	4164287	149265	7430115.0	4158229	323287.0	982430.0	5034623
26	Total	Tertiary	631844	9307109	9307109	22179969	7588778	1738217	30220377.00	19226568.0	...	992956	12256258	16717805	26015812	483103	53343788.0	28471410	1484709.0	5587975.0	45968959
27	12	TOTAL GSVA at basic prices	1636292	18666897	18666897	36184863	22228481	3597882	79131102.00	39828404.0	...	1821495	30255256	33918789	58158386	1467133	99515453.0	47528765	2911319.0	15076812.0	97516938
28	13	Taxes on Products	70099	1725309	1725309	3213546	2601791	527279	12353171.04	4985670.0	...	57674	3151184	3794100	5394503	72200	12507325.0	4425700	149345.0	1434856.0	10107396
29	14	Subsidies on products	30272	582406	582406	2006421	1332092	61854	1981546.00	1067867.0	...	37745	1209349	911800	2333442	18400	2766405.0	836700	94002.0	313139.0	3287219
30	15	Gross State Domestic Product	1676119	19809800	19809800	37391988	23498180	4063307	89502727.00	43746207.0	...	1841424	32197092	36801089	61219447	1520933	109256373.0	51117765	2966662.0	16198529.0	104337115
31	16	Population ('00)	14870	326780	326780	1101240	270530	14950	633590.00	266620.0	...	20550	435220	290673	721610	6330	745760.0	367660	38350.0	105820.0	2109940
32	17	Per Capita GSDP (Rs.)	112718	60621	60621	33954	86860	271793	141263.00	164077.0	...	89607	73979	126606	84837	240274	146503.0	139035	77358.0	153076.0	49450

	GDP per capita
Uttrakhand	153076.0
Kerala	154778.0
Haryana	164077.0
Sikkim	240274
Goa	271793

	Andhra Pradesh	Arunachal Pradesh	Assam	Bihar	Chhatisgarh	Goa	Gujarat	Haryana	Himachal Pradesh	Jharkhand	...	Nagaland	Odisha	Punjab	Rajasthan	Sikkim	Tamil Nadu	Telangana	Tripura	Uttrakhand	Uttar Pradesh
Item
Primary	716959	5326697	5326697	8019997	6400817	312129	15887187.0	8040424.0	1548366	5248354	...	616178	9009306	9296070	19113780	138776	13329774.0	9133354	942216.0	1845972.0	25999255
Secondary	287489	4033091	4033091	5984896	8238886	1547536	33023538.0	12561411.0	4119162	6241471	...	212361	8989693	7904914	13028794	845253	32841892.0	9924001	484393.0	7642865.0	25548724
Tertiary	631844	9307109	9307109	22179969	7588778	1738217	30220377.0	19226568.0	4133326	8133341	...	992956	12256258	16717805	26015812	483103	53343788.0	28471410	1484709.0	5587975.0	45968959
Gross State Domestic Product	1676119	19809800	19809800	37391988	23498180	4063307	89502727.0	43746207.0	10436879	21710718	...	1841424	32197092	36801089	61219447	1520933	109256373.0	51117765	2966662.0	16198529.0	104337115

	Andhra Pradesh	Arunachal Pradesh	Assam	Bihar	Chhatisgarh	Goa	Gujarat	Haryana	Himachal Pradesh	Jharkhand	...	Nagaland	Odisha	Punjab	Rajasthan	Sikkim	Tamil Nadu	Telangana	Tripura	Uttrakhand	Uttar Pradesh
Item
Primary	7.169590e+05	5.326697e+06	5.326697e+06	8.019997e+06	6.400817e+06	3.121290e+05	1.588719e+07	8.040424e+06	1.548366e+06	5.248354e+06	...	6.161780e+05	9.009306e+06	9.296070e+06	1.911378e+07	1.387760e+05	1.332977e+07	9.133354e+06	9.422160e+05	1.845972e+06	2.599926e+07
Secondary	2.874890e+05	4.033091e+06	4.033091e+06	5.984896e+06	8.238886e+06	1.547536e+06	3.302354e+07	1.256141e+07	4.119162e+06	6.241471e+06	...	2.123610e+05	8.989693e+06	7.904914e+06	1.302879e+07	8.452530e+05	3.284189e+07	9.924001e+06	4.843930e+05	7.642865e+06	2.554872e+07
Tertiary	6.318440e+05	9.307109e+06	9.307109e+06	2.217997e+07	7.588778e+06	1.738217e+06	3.022038e+07	1.922657e+07	4.133326e+06	8.133341e+06	...	9.929560e+05	1.225626e+07	1.671780e+07	2.601581e+07	4.831030e+05	5.334379e+07	2.847141e+07	1.484709e+06	5.587975e+06	4.596896e+07
Gross State Domestic Product	1.676119e+06	1.980980e+07	1.980980e+07	3.739199e+07	2.349818e+07	4.063307e+06	8.950273e+07	4.374621e+07	1.043688e+07	2.171072e+07	...	1.841424e+06	3.219709e+07	3.680109e+07	6.121945e+07	1.520933e+06	1.092564e+08	5.111776e+07	2.966662e+06	1.619853e+07	1.043371e+08
primary_percentage	4.277495e+01	2.688920e+01	2.688920e+01	2.144844e+01	2.723963e+01	7.681649e+00	1.775051e+01	1.837971e+01	1.483553e+01	2.417402e+01	...	3.346204e+01	2.798174e+01	2.526031e+01	3.122175e+01	9.124399e+00	1.220045e+01	1.786728e+01	3.176014e+01	1.139592e+01	2.491851e+01
secondary_percentage	1.715206e+01	2.035907e+01	2.035907e+01	1.600582e+01	3.506180e+01	3.808563e+01	3.689668e+01	2.871429e+01	3.946737e+01	2.874834e+01	...	1.153243e+01	2.792082e+01	2.148011e+01	2.128212e+01	5.557464e+01	3.005947e+01	1.941400e+01	1.632788e+01	4.718246e+01	2.448671e+01
tertiary_percentage	3.769685e+01	4.698235e+01	4.698235e+01	5.931744e+01	3.229517e+01	4.277838e+01	3.376476e+01	4.395025e+01	3.960308e+01	3.746233e+01	...	5.392327e+01	3.806635e+01	4.542747e+01	4.249599e+01	3.176360e+01	4.882442e+01	5.569768e+01	5.004645e+01	3.449681e+01	4.405811e+01

Item	Primary	Secondary	Tertiary	Gross State Domestic Product	primary_percentage	secondary_percentage	tertiary_percentage
Mizoram	225598.0	270072.0	637619.0	1155933.0	19.516529	23.363984	55.160550
Sikkim	138776.0	845253.0	483103.0	1520933.0	9.124399	55.574637	31.763595
Andhra Pradesh	716959.0	287489.0	631844.0	1676119.0	42.774946	17.152064	37.696846
Nagaland	616178.0	212361.0	992956.0	1841424.0	33.462038	11.532434	53.923268
Meghalaya	451050.0	637942.0	1200655.0	2440807.0	18.479544	26.136520	49.190903
Tripura	942216.0	484393.0	1484709.0	2966662.0	31.760140	16.327880	50.046450
Goa	312129.0	1547536.0	1738217.0	4063307.0	7.681649	38.085628	42.778382
Himachal Pradesh	1548366.0	4119162.0	4133326.0	10436879.0	14.835527	39.467373	39.603084
Uttrakhand	1845972.0	7642865.0	5587975.0	16198529.0	11.395924	47.182463	34.496805
Assam	5326697.0	4033091.0	9307109.0	19809800.0	26.889201	20.359070	46.982347
Arunachal Pradesh	5326697.0	4033091.0	9307109.0	19809800.0	26.889201	20.359070	46.982347
Jharkhand	5248354.0	6241471.0	8133341.0	21710718.0	24.174023	28.748340	37.462331
Chhatisgarh	6400817.0	8238886.0	7588778.0	23498180.0	27.239629	35.061805	32.295173
Odisha	9009306.0	8989693.0	12256258.0	32197092.0	27.981738	27.920823	38.066351
Punjab	9296070.0	7904914.0	16717805.0	36801089.0	25.260312	21.480109	45.427474
Bihar	8019997.0	5984896.0	22179969.0	37391988.0	21.448437	16.005825	59.317437
Haryana	8040424.0	12561411.0	19226568.0	43746207.0	18.379705	28.714286	43.950252
Madhya Pradesh	17854020.0	10044889.0	18117360.0	48198169.0	37.042942	20.840810	37.589312
Telangana	9133354.0	9924001.0	28471410.0	51117765.0	17.867280	19.413996	55.697682
Kerala	6489442.0	12070040.0	29673778.0	52600230.0	12.337288	22.946744	56.413780
Rajasthan	19113780.0	13028794.0	26015812.0	61219447.0	31.221746	21.282116	42.495993
Gujarat	15887187.0	33023538.0	30220377.0	89502727.0	17.750506	36.896684	33.764756
Karnataka	12066304.0	20484404.0	50490630.0	92178806.0	13.090107	22.222466	54.774663
Uttar Pradesh	25999255.0	25548724.0	45968959.0	104337115.0	24.918511	24.486707	44.058108
Tamil Nadu	13329774.0	32841892.0	53343788.0	109256373.0	12.200454	30.059475	48.824418
Maharashtra	21758383.0	47445207.0	88631076.0	179212165.0	12.141131	26.474323	49.455948

	S.No.	Item	Kerala	Haryana	Sikkim	Goa
0	1	Agriculture, forestry and fishing	5930617.0	8015238.0	137447	308507
5	2	Mining and quarrying	558824.0	25186.0	1329	3622
7	3	Manufacturing	4273567.0	7756921.0	550697	1177608
8	4	Electricity, gas, water supply & other utility...	482470.0	1101919.0	212499	204110
9	5	Construction	7314003.0	3702571.0	82058	165819
11	6	Trade, repair, hotels and restaurants	8557345.0	4986319.0	70568	380927
14	7	Transport, storage, communication & services r...	4020934.0	2560623.0	47347	189656
22	8	Financial services	2010306.0	1671486.0	21079	233618
23	9	Real estate, ownership of dwelling & professio...	7287633.0	6970183.0	75330	407099
24	10	Public administration	2068915.0	1036377.0	119514	346486
25	11	Other services	5728645.0	2001581.0	149265	180431
30	15	Gross State Domestic Product	52600230.0	43746207.0	1520933	4063307
32	17	Per Capita GSDP (Rs.)	154778.0	164077.0	240274	271793

	Level of Education - State	Primary - 2014-2015.1	Upper Primary - 2014-2015	Secondary - 2014-2015
0	A & N Islands	0.51	1.69	9.870
1	Andhra Pradesh	6.72	5.20	15.710
2	Arunachal Pradesh	10.82	6.71	17.110
3	Assam	15.36	10.51	27.060
4	Bihar	NaN	4.08	25.900
5	Chandigarh	NaN	0.44	NaN
6	Chhatisgarh	2.91	5.85	21.260
7	Dadra & Nagar Haveli	1.47	4.02	16.770
8	Daman & Diu	1.11	3.11	32.270
9	Delhi	NaN	0.76	11.810
10	Goa	0.73	0.07	11.150
11	Gujarat	0.89	6.41	25.040
12	Haryana	5.61	5.81	15.890
13	Himachal Pradesh	0.64	0.87	6.070
14	Jammu and Kashmir	6.79	5.44	17.280
15	Jharkhand	5.48	8.99	24.000
16	Karnataka	2.02	3.85	26.180
17	Kerala	NaN	NaN	12.320
18	Lakshadweep	NaN	2.78	6.763
19	Madhya Pradesh	6.59	9.20	24.770
20	Maharashtra	1.26	1.79	12.870
21	Manipur	9.66	4.20	14.380
22	Meghalaya	9.46	6.52	20.520
23	Mizoram	10.10	4.78	21.880
24	Nagaland	5.61	7.92	18.230
25	Odisha	2.86	3.81	29.560
26	Puducherry	0.37	0.56	12.190
27	Punjab	3.05	3.22	8.860
28	Rajasthan	5.02	3.07	13.480
29	Sikkim	2.27	1.57	15.890
30	Tamil Nadu	NaN	NaN	8.100
31	Telangana	2.08	2.30	15.530
32	Tripura	1.28	1.99	28.420
33	Uttar Pradesh	8.58	2.70	10.220
34	Uttrakhand	4.04	1.19	10.400
35	West Bengal	1.47	4.30	17.800
36	All India	4.13	4.03	17.060

	Level of education - State	Primary - 2014-2015.1	Upper Primary - 2014-2015	Secondary - 2014-2015	GDP per capita
0	Andhra Pradesh	6.72	5.20	15.71	112718
1	Arunachal Pradesh	10.82	6.71	17.11	60621
2	Assam	15.36	10.51	27.06	60621
3	Bihar	NaN	4.08	25.90	33954
4	Chhatisgarh	2.91	5.85	21.26	86860
5	Goa	0.73	0.07	11.15	271793
6	Gujarat	0.89	6.41	25.04	141263.0
7	Haryana	5.61	5.81	15.89	164077.0
8	Himachal Pradesh	0.64	0.87	6.07	147330
9	Jharkhand	5.48	8.99	24.00	62091
10	Karnataka	2.02	3.85	26.18	145141
11	Kerala	NaN	NaN	12.32	154778.0
12	Madhya Pradesh	6.59	9.20	24.77	62989
13	Maharashtra	1.26	1.79	12.87	152853
14	Manipur	9.66	4.20	14.38	NaN
15	Meghalaya	9.46	6.52	20.52	76228.0
16	Mizoram	10.10	4.78	21.88	97687
17	Nagaland	5.61	7.92	18.23	89607
18	Odisha	2.86	3.81	29.56	73979
19	Punjab	3.05	3.22	8.86	126606
20	Rajasthan	5.02	3.07	13.48	84837
21	Sikkim	2.27	1.57	15.89	240274
22	Tamil Nadu	NaN	NaN	8.10	146503.0
23	Telangana	2.08	2.30	15.53	139035
24	Tripura	1.28	1.99	28.42	77358.0
25	Uttar Pradesh	8.58	2.70	10.22	49450
26	Uttrakhand	4.04	1.19	10.40	153076.0

	Primary - 2014-2015.1	Upper Primary - 2014-2015	Secondary - 2014-2015
count	24.000000	25.000000	27.000000
mean	5.126667	4.504400	17.807407
std	3.890927	2.781644	6.845367
min	0.640000	0.070000	6.070000
25%	2.065000	2.300000	12.595000
50%	4.530000	4.080000	15.890000
75%	7.185000	6.410000	24.385000
max	15.360000	10.510000	29.560000

	Item	Percentage of Total GDP	Cumulative sum
0	Real estate, ownership of dwelling & professio...	105.323884	105.323884
1	Agriculture, forestry and fishing	102.834194	208.158078
2	Trade, repair, hotels and restaurants	100.000000	308.158078
3	Manufacturing	98.311087	406.469166
4	Construction	80.488196	486.957361
5	Other services	57.590785	544.548147
6	Transport, storage, communication & services r...	48.720847	593.268994
7	Financial services	28.127505	621.396499
8	Public administration	25.518052	646.914551
9	Electricity, gas, water supply & other utility...	14.297787	661.212338
10	Mining and quarrying	4.208319	665.420657

	Item	Percentage of Total GDP	Cumulative sum
0	Manufacturing	178.312474	178.312474
1	Real estate, ownership of dwelling & professio...	150.429716	328.742190
2	Agriculture, forestry and fishing	122.812573	451.554763
3	Trade, repair, hotels and restaurants	100.000000	551.554763
4	Construction	66.385234	617.939997
5	Financial services	59.270602	677.210599
6	Other services	59.200409	736.411008
7	Transport, storage, communication & services r...	55.111749	791.522757
8	Public administration	30.320314	821.843071
9	Electricity, gas, water supply & other utility...	22.572885	844.415956
10	Mining and quarrying	16.939155	861.355111

	Item	Percentage of Total GDP	Cumulative sum
0	Agriculture, forestry and fishing	210.633751	210.633751
1	Manufacturing	133.462853	344.096604
2	Trade, repair, hotels and restaurants	100.000000	444.096604
3	Real estate, ownership of dwelling & professio...	90.829939	534.926543
4	Construction	84.868654	619.795197
5	Mining and quarrying	71.868555	691.663752
6	Other services	66.093515	757.757267
7	Transport, storage, communication & services r...	54.984246	812.741512
8	Public administration	44.007962	856.749475
9	Financial services	29.706348	886.455823
10	Electricity, gas, water supply & other utility...	26.537574	912.993397

	Item	Percentage of Total GDP	Cumulative sum
0	Agriculture, forestry and fishing	176.939764	176.939764
1	Trade, repair, hotels and restaurants	100.000000	276.939764
2	Real estate, ownership of dwelling & professio...	91.627222	368.566986
3	Manufacturing	90.826828	459.393814
4	Construction	80.330587	539.724401
5	Other services	58.704964	598.429365
6	Transport, storage, communication & services r...	57.988309	656.417674
7	Public administration	48.552186	704.969860
8	Financial services	24.984784	729.954644
9	Mining and quarrying	23.939185	753.893829
10	Electricity, gas, water supply & other utility...	13.304970	767.198799